Motion_Object_Detection.py

import cv2, time, pandas
from datetime import datetime

first_frame = None
status_list = [None, None]
times = []
df = pandas.DataFrame(columns = ["Start", "End"])

video = cv2.VideoCapture(0)

a = 1
while True:
    a = a + 1
    check, frame = video.read()
    status = 0
    gray = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
    gray = cv2.GaussianBlur(gray,(21,21), 0)

    if first_frame is None:
        first_frame = gray
        continue

    delta_frame = cv2.absdiff(first_frame,gray)
    thresh_frame = cv2.threshold(delta_frame,30,255,cv2.THRESH_BINARY)[1]
    thresh_frame = cv2.dilate(thresh_frame, None, iterations =0)

    (cnts,_) = cv2.findContours(thresh_frame.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)

    for contour in cnts:
        if cv2.contourArea(contour) < 1000:
            continue
        status = 1
        (x, y, w, h) = cv2.boundingRect(contour)
        cv2.rectangle(frame,(x,y), (x+w,y+h),(0,255,0),3)

    status_list.append(status)
    status_list=status_list[-2:]

    if status_list[-1]==1 and status_list[-2]==0:
        times.append(datetime.now())
    if status_list[-1]==0 and status_list[-2]==1:
        times.append(datetime.now())

    cv2.imshow('Gray Frame', gray)
    cv2.imshow('Delta Frame', delta_frame)
    cv2.imshow('Threshold Frame', thresh_frame)
    cv2.imshow('Color Frame', frame)

    key = cv2.waitKey(1)

    if key ==ord('q'):
        if status == 1:
            times.append(datetime.now())
            break

print(status_list)
print(times)

for i in range(0, len(times), 2):
    df = df.append({"Start":times[i], "End":times[i+1]}, ignore_index= True)

df.to_csv('Times.csv')

video.release()
cv2.destroyAllWindows